scholarly journals Cooperative Attack-Defense Decision-Making of Multi-UAV Using Satisficing Decision-Enhanced Wolf Pack Search Algorithm

2021 ◽  
Author(s):  
Tongle Zhou ◽  
Mou Chen ◽  
Yuhui Wang ◽  
Ronggang Zhu ◽  
Chenguang Yang
Keyword(s):  
Decision Making ◽  
Unmanned Aerial Vehicles ◽  
Decision Problem ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Target Information ◽  
Aerial Vehicles ◽  
Air Combat ◽  
Simulation Results ◽  
Multi Uav

Abstract Unmanned Aerial Vehicles (UAVs) have shown their superiority for applications in complicated military missions. A cooperative attack-defense decision-making method based on satisficing decision-enhanced wolf pack search (SDEWPS) algorithm is developed for multi-UAV air combat in this paper. Firstly, the multi-UAV air combat mathematical model is provided and the attack-defense decision-making constraints are defined. Besides the traditional air combat situation, the capability of UAVs and target information including target type and target intention are all considered in this paper to establish the air combat superiority function. Then, the wolf pack search (WPS) algorithm is used to solve the attack decision problem. In order to improve efficiency, the satisficing decision theory is employed to enhance the WPS to obtain the satisficing solution rather than optimal solution. The simulation results show that the developed method can realize the cooperative attack decision-making.

scholarly journals Model-Based Approach to Engineering Resilience in Multi-UAV Systems

Systems ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 11 ◽  
Author(s):  
Edwin Ordoukhanian ◽  
Azad Madni
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Research Community ◽  
Operational Environment ◽  
System Operation ◽  
Aerial Vehicles ◽  
Simulation Based ◽  
Simulation Results ◽  
Time Evaluation ◽  
Context Simulation ◽  
Multi Uav

Multi-UAV Operations are an area of great interest in government, industry, and research community. In multi-UAV operations, a group of unmanned aerial vehicles (UAVs) are deployed to carry out missions such as search and rescue or disaster relief. As multi-UAV systems operate in an open operational environment, many disrupting events can occur. To this end, resilience of these systems is of great importance. The research performed and reported in this paper utilizes simulation-based research methodology and demonstrates that resilience of multi-UAV systems can be achieved by real-time evaluation of resilience alternatives during system operation. This evaluation is done using a dynamic utility function where priorities change as a function of context. Simulation results show that resilience response can in fact change depending on the context.

scholarly journals Autonomous maneuver strategy of swarm air combat based on DDPG

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Luhe Wang ◽  
Jinwen Hu ◽  
Zhao Xu ◽  
Chunhui Zhao
Keyword(s):  
Decision Making ◽  
Unmanned Aerial Vehicles ◽  
Strategy Training ◽  
Motion Model ◽  
High Complexity ◽  
Aerial Vehicles ◽  
Air Combat ◽  
Policy Gradient ◽  
Online Strategy ◽  
Visual Range

AbstractUnmanned aerial vehicles (UAVs) have been found significantly important in the air combats, where intelligent and swarms of UAVs will be able to tackle with the tasks of high complexity and dynamics. The key to empower the UAVs with such capability is the autonomous maneuver decision making. In this paper, an autonomous maneuver strategy of UAV swarms in beyond visual range air combat based on reinforcement learning is proposed. First, based on the process of air combat and the constraints of the swarm, the motion model of UAV and the multi-to-one air combat model are established. Second, a two-stage maneuver strategy based on air combat principles is designed which include inter-vehicle collaboration and target-vehicle confrontation. Then, a swarm air combat algorithm based on deep deterministic policy gradient strategy (DDPG) is proposed for online strategy training. Finally, the effectiveness of the proposed algorithm is validated by multi-scene simulations. The results show that the algorithm is suitable for UAV swarms of different scales.

Intelligent cooperative collision avoidance via fuzzy potential fields

Robotica ◽  
2021 ◽  
pp. 1-20
Author(s):  
Daegyun Choi ◽  
Anirudh Chhabra ◽  
Donghoon Kim
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Collision Avoidance ◽  
Fuzzy Inference ◽  
Potential Fields ◽  
Rule Base ◽  
Local Minima ◽  
Inference System ◽  
Aerial Vehicles ◽  
Different Shapes ◽  
Simulation Results

Summary This paper proposes an intelligent cooperative collision avoidance approach combining the enhanced potential field (EPF) with a fuzzy inference system (FIS) to resolve local minima and goal non-reachable with obstacles nearby issues and provide a near-optimal collision-free trajectory. A genetic algorithm is utilized to optimize parameters of membership function and rule base of the FISs. This work uses a single scenario containing all issues and interactions among unmanned aerial vehicles (UAVs) for training. For validating the performance, two scenarios containing obstacles with different shapes and several UAVs in small airspace are considered. Multiple simulation results show that the proposed approach outperforms the conventional EPF approach statistically.

scholarly journals Distributed Cooperative Avoidance Control for Multi-Unmanned Aerial Vehicles

Actuators ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 1 ◽  
Author(s):  
Sunan Huang ◽  
Rodney Swee Huat Teo ◽  
Wenqi Liu
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Case Studies ◽  
Collision Avoidance ◽  
Hardware In The Loop ◽  
Crucial Issue ◽  
Communication Failure ◽  
Aerial Vehicles ◽  
Avoidance Control ◽  
Velocity Obstacle ◽  
Multi Uav

It is well-known that collision-free control is a crucial issue in the path planning of unmanned aerial vehicles (UAVs). In this paper, we explore the collision avoidance scheme in a multi-UAV system. The research is based on the concept of multi-UAV cooperation combined with information fusion. Utilizing the fused information, the velocity obstacle method is adopted to design a decentralized collision avoidance algorithm. Four case studies are presented for the demonstration of the effectiveness of the proposed method. The first two case studies are to verify if UAVs can avoid a static circular or polygonal shape obstacle. The third case is to verify if a UAV can handle a temporary communication failure. The fourth case is to verify if UAVs can avoid other moving UAVs and static obstacles. Finally, hardware-in-the-loop test is given to further illustrate the effectiveness of the proposed method.

Forest Firefighting Monitoring System Based on UAV Team and Remote Sensing

2019 ◽  
pp. 220-241
Author(s):  
Maryna Zharikova ◽  
Vladimir Sherstjuk
Keyword(s):  
Remote Sensing ◽  
Data Integration ◽  
Unmanned Aerial Vehicles ◽  
Monitoring System ◽  
Fire Response ◽  
Ground Support ◽  
Aerial Vehicles ◽  
Remote Sensing Techniques ◽  
Fire Spreading ◽  
Multi Uav

In this chapter, the authors propose an approach to using a heterogeneous team of unmanned aerial vehicles and remote sensing techniques to perform tactical forest firefighting operations. The authors present the three-level architecture of the multi-UAV-based forest firefighting monitoring system; features of patrolling, confirming, and monitoring missions; as well as functions of UAV in such missions. The authors consider an infrastructure for the UAV ground support and equipment used for the UAVs control. The method of the data integration into a fire-spreading model in a real-time DSS for the forest fire response is proposed. The proposed approach has been tested with the multi-UAV team that included three drones for the patrol missions, one helicopter for the confirmation mission, and one octocopter for the monitoring mission. The performance of such multi-UAV team has been studied in the laboratory conditions. The result of the experiment has shown that the proposed approach provides required credibility and efficiency of fire prediction and response.

scholarly journals Selection of Unmanned Aerial Vehicles by Using Multicriteria Decision-Making for Defence

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Mustafa Hamurcu ◽  
Tamer Eren
Keyword(s):  
Decision Making ◽  
Unmanned Aerial Vehicles ◽  
Selection Process ◽  
Critical Role ◽  
Multicriteria Decision Making ◽  
Critical Factor ◽  
Unmanned Systems ◽  
Multicriteria Decision ◽  
Aerial Vehicles ◽  
Selection Of

The unmanned systems have been seeing a significant boom in the last ten years in different areas together with technological developments. One of the unmanned systems is unmanned aerial vehicles (UAVs). UAVs are used for reconnaissance and observation in the military areas and play critical role in attack and destroy missions. These vehicles have been winning more features together with developing technology in todays world. In addition, they have been varying with different features. A systematic and efficient approach for the selection of the UAV is necessary to choose a best alternative for the critical tasks under consideration. The multicriteria decision-making (MCDM) approaches that are analytic processes are well suited to deal intricacy in selection of alternative vehicles. This study also proposes an integrated methodology based on the analytic hierarch process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) to evaluate UAV alternatives for selection process. Firstly, AHP, a MCDM method, is used to determine the weights of each critical factor. Subsequently, it is utilized with the TOPSIS approach to rank the vehicle alternatives in the decision problem. Result of the study shows that UAV-1 was selected as the most suitable vehicle. In results, it is seen that the weights of the evaluation criteria found by using AHP affect the decision-making process. Finally, the validation and sensitivity analysis of the solution are made and discussed.

Multi-UAV Specification and Control with a Single Pilot-in-the-Loop

2020 ◽  
Vol 08 (04) ◽  
pp. 269-277
Author(s):  
Patricio Moreno ◽  
Santiago Esteva ◽  
Ignacio Mas ◽  
Juan I. Giribet
Keyword(s):  
Remote Control ◽  
Unmanned Aerial Vehicles ◽  
Field Experiments ◽  
Aerial Vehicles ◽  
Simulated Environment ◽  
Aerial Vehicle ◽  
Space Formulation ◽  
Trajectory Following ◽  
And Control ◽  
Multi Uav

This work presents a multi-unmanned aerial vehicle formation implementing a trajectory-following controller based on the cluster-space robot coordination method. The controller is augmented with a feed-forward input from a control station operator. This teleoperation input is generated by means of a remote control, as a simple way of modifying the trajectory or taking over control of the formation during flight. The cluster-space formulation presents a simple specification of the system’s motion and, in this work, the operator benefits from this capability to easily evade obstacles by means of controlling the cluster parameters in real time. The proposed augmented controller is tested in a simulated environment first, and then deployed for outdoor field experiments. Results are shown in different scenarios using a cluster of three autonomous unmanned aerial vehicles.

Sign in / Sign up

Export Citation Format

Share Document